Purpose :
An accurate census of lipofuscin (L), melanolipofuscin (ML) and melanosomes (M) in human RPE cells is needed for theories of aging and RPE-driven disease. We compared different subclasses of RPE granules in normal eyes of young and older individuals by 3D SBFSEM of archived specimens.

Methods :
Six eyes of white donors with unremarkable maculas were post-fixed with osmium tannic acid paraphenylenediamine. Epoxy-embedded samples were sectioned and imaged using an SEM fitted with an in-chamber automated ultramicrotome. Aligned tissue volumes were generated by alternately imaging the block face using backscattered electrons and removing a 125 nm-thick layer. Custom ImageJ software was used to assign x,y,z locations of 7 organelle classes and boundaries of individual cells in image stacks.

Results :
A younger age group consisted of donors 16M, 32F and 40M, while an older group contained specimens 76F, 84M and 85F. Series of 249-499 sections in each eye containing 5-25 nuclei were examined. The 3 granule types were visualized within the volumetric dataset based on distinct features. L (Fig. 1A-D) were round or oval with homogenous electron-dense (ED) interiors with (L1,2) or without (L3,4) an ED rim or ED condensations around the granule. M were spindle-shaped with homogenous ED contents (M, Fig. 1E,F). ML (Fig. 1G,H) contained an ED spindle within a larger granule (ML1,2). L and ML were further separated by size with L1/3 and ML1 representing granules equal or larger and L2/4 and ML2 smaller than 950 nm in diameter. Sample 16M was notable for abundant L, M, as well as small ML compared to older specimens. ML were numerous across all advanced age donors. Overall a fair amount of variability was observed in this cohort. Individual numbers of granules of different classes are listed in the table.

Conclusions :
Human RPE contains a variable number of organelles of different sizes in these age groups. Similar information from a larger dataset in progress will aid hypothesis testing about organelle turnover and regulation in health, aging, and disease, and the interpretation of clinical imaging technologies.

This is an abstract that was submitted for the 2017 ARVO Annual Meeting, held in Baltimore, MD, May 7-11, 2017.